Michigan Engineering | University of Michigan The project, which received a best paper award, demonstrated that a certain bias in humans who train intelligent agents significantly reduced the effectiveness of the training.
Research on human biases in AI learning earns best student paper award
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Get to know: Xinyu Wang
“My research has the potential to democratize programming and make it possible for millions of people around the globe to automate otherwise tedious tasks using programming.”
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How predictive modeling could help us reopen more safely
Graphical online simulation could spur more targeted COVID-19 protection measures.
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Faster than COVID: a computer model that predicts the disease’s next move
Predictive model could help care providers stay safe, anticipate patient needs.
The post Faster than COVID: a computer model that predicts the disease’s next move appeared first on Michigan Engineering News.
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Faculty Profile: Emily Mower Provost
Mower Provost talks about getting awards, doing industry research, understanding human behavior – and Star Wars.
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Research team takes on food insecurity in Detroit in the face of coronavirus limitations
Researchers are working with the city on two key initiatives to address food availability for elderly and low-income populations.
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Building better coronavirus databases with automatic quality checks
The team will build high-quality datasets to enable automatic quality checking and fraud detection of the new coronavirus data.
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Computer scientists employ AI to help address COVID-19 challenges
Five multidisciplinary research teams are working on projects to assist with the coronavirus outbreak and to help find solutions to pressing problems.
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Emily Mower Provost named Toyota Faculty Scholar
Her work uses machine learning to measure mood, emotion, and other aspects of human behavior for purposes of providing early or real-time interventions for people in managing their health.
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Undergraduate research on speeding up data centers earns ACM first prize
The student’s project targets critical moments where the next instruction in a program is only available in a slower type of memory.